| data science apprenticeship program: Hadoop For Dummies Dirk deRoos, 2014-04-14 Let Hadoop For Dummies help harness the power of your data and rein in the information overload Big data has become big business, and companies and organizations of all sizes are struggling to find ways to retrieve valuable information from their massive data sets with becoming overwhelmed. Enter Hadoop and this easy-to-understand For Dummies guide. Hadoop For Dummies helps readers understand the value of big data, make a business case for using Hadoop, navigate the Hadoop ecosystem, and build and manage Hadoop applications and clusters. Explains the origins of Hadoop, its economic benefits, and its functionality and practical applications Helps you find your way around the Hadoop ecosystem, program MapReduce, utilize design patterns, and get your Hadoop cluster up and running quickly and easily Details how to use Hadoop applications for data mining, web analytics and personalization, large-scale text processing, data science, and problem-solving Shows you how to improve the value of your Hadoop cluster, maximize your investment in Hadoop, and avoid common pitfalls when building your Hadoop cluster From programmers challenged with building and maintaining affordable, scaleable data systems to administrators who must deal with huge volumes of information effectively and efficiently, this how-to has something to help you with Hadoop. |
| data science apprenticeship program: Cross Reality and Data Science in Engineering Michael E. Auer, Dominik May, 2020-08-20 Today, online technologies are at the core of most fields of engineering and society as a whole . This book discusses the fundamentals, applications and lessons learned in the field of online and remote engineering, virtual instrumentation, and other related technologies like Cross Reality, Data Science & Big Data, Internet of Things & Industrial Internet of Things, Industry 4.0, Cyber Security, and M2M & Smart Objects. Since the first Remote Engineering and Virtual Instrumentation (REV) conference in 2004, the event has focused on the use of the Internet for engineering tasks, as well as the related opportunities and challenges. In a globally connected world, interest in online collaboration, teleworking, remote services, and other digital working environments is rapidly increasing. In this context, the REV conferences discuss fundamentals, applications and experiences in the field of Online and Remote Engineering as well as Virtual Instrumentation. Furthermore, the conferences focus on guidelines and new concepts for engineering education in higher and vocational education institutions, including emerging technologies in learning, MOOCs & MOOLs, and open resources. This book presents the proceedings of REV2020 on “Cross Reality and Data Science in Engineering” which was held as the 17th in series of annual events. It was organized in cooperation with the Engineering Education Transformations Institute and the Georgia Informatics Institutes for Research and Education and was held at the College of Engineering at the University of Georgia in Athens (GA), USA, from February 26 to 28, 2020. |
| data science apprenticeship program: Apprenticeship Patterns Dave Hoover, Adewale Oshineye, 2009-10-02 Are you doing all you can to further your career as a software developer? With today's rapidly changing and ever-expanding technologies, being successful requires more than technical expertise. To grow professionally, you also need soft skills and effective learning techniques. Honing those skills is what this book is all about. Authors Dave Hoover and Adewale Oshineye have cataloged dozens of behavior patterns to help you perfect essential aspects of your craft. Compiled from years of research, many interviews, and feedback from O'Reilly's online forum, these patterns address difficult situations that programmers, administrators, and DBAs face every day. And it's not just about financial success. Apprenticeship Patterns also approaches software development as a means to personal fulfillment. Discover how this book can help you make the best of both your life and your career. Solutions to some common obstacles that this book explores in-depth include: Burned out at work? Nurture Your Passion by finding a pet project to rediscover the joy of problem solving. Feeling overwhelmed by new information? Re-explore familiar territory by building something you've built before, then use Retreat into Competence to move forward again. Stuck in your learning? Seek a team of experienced and talented developers with whom you can Be the Worst for a while. Brilliant stuff! Reading this book was like being in a time machine that pulled me back to those key learning moments in my career as a professional software developer and, instead of having to learn best practices the hard way, I had a guru sitting on my shoulder guiding me every step towards master craftsmanship. I'll certainly be recommending this book to clients. I wish I had this book 14 years ago!-Russ Miles, CEO, OpenCredo |
| data science apprenticeship program: Data Science in the Public Interest: Improving Government Performance in the Workforce Joshua D. Hawley, 2020-07-22 This book is about how new and underutilized types of big data sources can inform public policy decisions related to workforce development. Hawley describes how government is currently using data to inform decisions about the workforce at the state and local levels. He then moves beyond standardized performance metrics designed to serve federal agency requirements and discusses how government can improve data gathering and analysis to provide better, up-to-date information for government decision making. |
| data science apprenticeship program: Applied Data Science Martin Braschler, Thilo Stadelmann, Kurt Stockinger, 2019-06-13 This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other. With these goals in mind, the book is divided into three parts: Part I pays tribute to the interdisciplinary nature of data science and provides a common understanding of data science terminology for readers with different backgrounds. These six chapters are geared towards drawing a consistent picture of data science and were predominantly written by the editors themselves. Part II then broadens the spectrum by presenting views and insights from diverse authors – some from academia and some from industry, ranging from financial to health and from manufacturing to e-commerce. Each of these chapters describes a fundamental principle, method or tool in data science by analyzing specific use cases and drawing concrete conclusions from them. The case studies presented, and the methods and tools applied, represent the nuts and bolts of data science. Finally, Part III was again written from the perspective of the editors and summarizes the lessons learned that have been distilled from the case studies in Part II. The section can be viewed as a meta-study on data science across a broad range of domains, viewpoints and fields. Moreover, it provides answers to the question of what the mission-critical factors for success in different data science undertakings are. The book targets professionals as well as students of data science: first, practicing data scientists in industry and academia who want to broaden their scope and expand their knowledge by drawing on the authors’ combined experience. Second, decision makers in businesses who face the challenge of creating or implementing a data-driven strategy and who want to learn from success stories spanning a range of industries. Third, students of data science who want to understand both the theoretical and practical aspects of data science, vetted by real-world case studies at the intersection of academia and industry. |
| data science apprenticeship program: Building America's Skilled Technical Workforce National Academies of Sciences, Engineering, and Medicine, National Academy of Engineering, Division of Behavioral and Social Sciences and Education, Policy and Global Affairs, Board on Science Education, Board on Higher Education and Workforce, Board on Science, Technology, and Economic Policy, Committee on the Supply Chain for Middle-Skill Jobs: Education, Training, and Certification Pathways, 2017-06-04 Skilled technical occupationsâ€defined as occupations that require a high level of knowledge in a technical domain but do not require a bachelor's degree for entryâ€are a key component of the U.S. economy. In response to globalization and advances in science and technology, American firms are demanding workers with greater proficiency in literacy and numeracy, as well as strong interpersonal, technical, and problem-solving skills. However, employer surveys and industry and government reports have raised concerns that the nation may not have an adequate supply of skilled technical workers to achieve its competitiveness and economic growth objectives. In response to the broader need for policy information and advice, Building America's Skilled Technical Workforce examines the coverage, effectiveness, flexibility, and coordination of the policies and various programs that prepare Americans for skilled technical jobs. This report provides action-oriented recommendations for improving the American system of technical education, training, and certification. |
| data science apprenticeship program: Developing Analytic Talent Vincent Granville, 2014-03-24 Learn what it takes to succeed in the the most in-demand tech job Harvard Business Review calls it the sexiest tech job of the 21st century. Data scientists are in demand, and this unique book shows you exactly what employers want and the skill set that separates the quality data scientist from other talented IT professionals. Data science involves extracting, creating, and processing data to turn it into business value. With over 15 years of big data, predictive modeling, and business analytics experience, author Vincent Granville is no stranger to data science. In this one-of-a-kind guide, he provides insight into the essential data science skills, such as statistics and visualization techniques, and covers everything from analytical recipes and data science tricks to common job interview questions, sample resumes, and source code. The applications are endless and varied: automatically detecting spam and plagiarism, optimizing bid prices in keyword advertising, identifying new molecules to fight cancer, assessing the risk of meteorite impact. Complete with case studies, this book is a must, whether you're looking to become a data scientist or to hire one. Explains the finer points of data science, the required skills, and how to acquire them, including analytical recipes, standard rules, source code, and a dictionary of terms Shows what companies are looking for and how the growing importance of big data has increased the demand for data scientists Features job interview questions, sample resumes, salary surveys, and examples of job ads Case studies explore how data science is used on Wall Street, in botnet detection, for online advertising, and in many other business-critical situations Developing Analytic Talent: Becoming a Data Scientist is essential reading for those aspiring to this hot career choice and for employers seeking the best candidates. |
| data science apprenticeship program: Apprentice Nation Ryan Craig, 2023-11-07 College isn’t for everyone. It’s time to challenge the status quo and embrace the potential of apprenticeships in tech, healthcare, finance, and more—which can provide a sustainable pathway to economic opportunity. For decades, college has been the only respectable way to access the world of work, despite paralyzing tuition and a dire lack of practical skills that has left 40 percent of college graduates underemployed, unfulfilled, and struggling to repay student loan debt. Education and workforce expert Ryan Craig explores how a modern apprenticeship system will allow students and job seekers to jump-start their careers by learning while they earn—ultimately leading to greater workforce diversity and geographic mobility. With a deep dive into the history behind America’s outdated college system, Craig reveals: The origins of the student debt crises and admissions scandals Why apprenticeships are an effective pathway to career opportunity What America can do to catch up with other nations making apprenticeship opportunities broadly available Where students and job seekers can go to land an apprenticeship Featuring a directory of US apprenticeship programs by industry and location, Apprentice Nation is an accessible blueprint for a country where young Americans of all backgrounds can launch careers in a variety of in-demand fields. With just a few common sense changes to education and workforce development, anapprentice nation will put the American Dream within reach—for everyone. |
| data science apprenticeship program: Roundtable on Data Science Postsecondary Education National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Division on Engineering and Physical Sciences, Board on Science Education, Computer Science and Telecommunications Board, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, 2020-10-02 Established in December 2016, the National Academies of Sciences, Engineering, and Medicine's Roundtable on Data Science Postsecondary Education was charged with identifying the challenges of and highlighting best practices in postsecondary data science education. Convening quarterly for 3 years, representatives from academia, industry, and government gathered with other experts from across the nation to discuss various topics under this charge. The meetings centered on four central themes: foundations of data science; data science across the postsecondary curriculum; data science across society; and ethics and data science. This publication highlights the presentations and discussions of each meeting. |
| data science apprenticeship program: The Future of Work Thomas A.Q.T. Truong, 2024-07-02 Artificial intelligence (AI) stands at the cusp of revolutionizing the global job market, echoing previous technological upheavals such as the Industrial Revolution. The Future of Work: How AI Will Transform Industries and Jobs delves deeply into the multifaceted impacts of AI on employment, offering a comprehensive framework to understand and navigate this landscape. The book opens by defining the conceptual and functional parameters of AI, emphasizing its capabilities to mimic human reasoning, learning, and task execution. Drawing parallels to historical technological shifts, it sets the stage for the profound changes AI brings. We then explore the fundamental distinction between job displacement—anxiety-inducing automation—and job transformation—opportunities for innovation and creativity. Through compelling statistics and predictions from authoritative sources like McKinsey and the World Economic Forum, the magnitude of AI’s influence becomes palpable. Next, the narrative zooms into specific industries profoundly affected by AI. It scrutinizes high-risk sectors such as administrative roles, highlighting the susceptibility of tasks like data entry to automation. Contrasted with this is the emergence of new roles in data analysis, blockchain technology, and AI specialization, which promise to reshape the employment landscape positively. The book does not shy away from addressing the inequities and disparities AI may exacerbate. It provides an incisive analysis of gender and geographic impacts, shedding light on how women and low-income countries face unique challenges. These sections thoughtfully consider nuanced aspects of how AI interacts with societal structures, urging for equitable solutions. Highlighting AI's potential to serve as a force multiplier in enhancing productivity, examples are presented from logistics optimization to autonomous negotiations, and manufacturing process improvements. These case studies showcase AI’s role in freeing humans from routine tasks to focus on more complex and rewarding activities, propelling businesses toward unprecedented efficiency and growth. Recognizing the necessity of proactive measures to manage the transition toward an AI-driven future, the book underscores the significance of upskilling and reskilling. Through inspiring company transformations and individual success stories, readers gain practical insights into adapting to new realities. Policies aimed at social support, redeployment, and rigorous training programs are advocated as crucial responses to potential job displacement. In its conclusion, the book balances a tone of cautious optimism. It acknowledges AI’s immense potential while prescribing vigilant and forward-thinking strategies to harmonize technological advancement with human-centric work values. The future outlook encourages readiness to continuously adapt, emphasizing the notion that AI evolution is an ongoing process set to redefine industries and job markets perpetually. The Future of Work: How AI Will Transform Industries and Jobs stands out for its unique perspective brought by an AI expert recognized as an MIT AI Innovator Under 35 Europe 2023. This work combines rigorous research with personal insights, offering a powerful, authoritative analysis necessary for anyone invested in understanding the future of the job market amid AI advancements. |
| data science apprenticeship program: The Mathematical Corporation Josh Sullivan, Angela Zutavern, 2017-06-06 The most powerful weapon in business today is the alliance between the mathematical smarts of machines and the imaginative human intellect of great leaders. Together they make the mathematical corporation, the business model of the future. We are at a once-in-a-decade breaking point similar to the quality revolution of the 1980s and the dawn of the internet age in the 1990s: leaders must transform how they run their organizations, or competitors will bring them crashing to earth -- often overnight. Mathematical corporations -- the organizations that will master the future -- will outcompete high-flying rivals by merging the best of human ingenuity with machine intelligence. While smart machines are weapon number one for organizations, leaders are still the drivers of breakthroughs. Only they can ask crucial questions to capitalize on business opportunities newly discovered in oceans of data. This dynamic combination will make possible the fulfillment of missions that once seemed out of reach, even impossible to attain. Josh Sullivan and Angela Zutavern's extraordinary examples include the entrepreneur who upended preventive health care, the oceanographer who transformed fisheries management, and the pharmaceutical company that used algorithm-driven optimization to boost vaccine yields. Together they offer a profoundly optimistic vision for a dazzling new phase in business, and a playbook for how smart companies can manage the essential combination of human and machine. |
| data science apprenticeship program: Growing Fairly Stephen Goldsmith, Kate Markin Coleman, 2022-02-22 Tested, practical ideas to meet current and future skilling needs of both workers and employers The labor market in the United States faces seemingly contradictory challenges: Many employers have trouble finding qualified applicants for current and future jobs, while millions of Americans are out of work or are underemployed—their paths to living-wage jobs blocked by systemic barriers or lack of adequate skills. Growing Fairly offers workforce development reforms that meet the needs of both workers and employers. Based on the experiences of hundreds of leaders and workers, the authors set out ten principles for designing a more effective and equitable system that helps workers obtain the skills necessary for economic mobility. The principles outlined in the book argue for a more comprehensive view of the skilling needs of current and prospective workers. They spell out the attributes of effective programs and make the case for skill-based hiring, widely distributed performance data, and collaboration. The book emphasizes the importance of local action to overcome the structural barriers that challenge even the most determined would-be learners. Growing Fairly shows cross sector leaders how to work across organizational boundaries to change the trajectory of individuals struggling to make a living wage. This is not a book of untested theories. Instead, it is written by practitioners for practitioners. Much of it is told through the voices of those who run programs and people who have taken advantage of them. While the issues the book addresses are profound, its take on the subject is optimistic. Between them, the authors have spent decades searching out and supporting effective practices. Even more critically, they have learned how to knit competing agencies and organizations into cohesive systems with coordinated missions. Their practical ideas will benefit a wide range of readers, from practitioners in the field to students and scholars of the American labor system. |
| data science apprenticeship program: Hands-On Data Analysis with Pandas Stefanie Molin, 2019-07-26 Get to grips with pandas—a versatile and high-performance Python library for data manipulation, analysis, and discovery Key FeaturesPerform efficient data analysis and manipulation tasks using pandasApply pandas to different real-world domains using step-by-step demonstrationsGet accustomed to using pandas as an effective data exploration toolBook Description Data analysis has become a necessary skill in a variety of positions where knowing how to work with data and extract insights can generate significant value. Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will learn how to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification, using scikit-learn, to make predictions based on past data. By the end of this book, you will be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. What you will learnUnderstand how data analysts and scientists gather and analyze dataPerform data analysis and data wrangling in PythonCombine, group, and aggregate data from multiple sourcesCreate data visualizations with pandas, matplotlib, and seabornApply machine learning (ML) algorithms to identify patterns and make predictionsUse Python data science libraries to analyze real-world datasetsUse pandas to solve common data representation and analysis problemsBuild Python scripts, modules, and packages for reusable analysis codeWho this book is for This book is for data analysts, data science beginners, and Python developers who want to explore each stage of data analysis and scientific computing using a wide range of datasets. You will also find this book useful if you are a data scientist who is looking to implement pandas in machine learning. Working knowledge of Python programming language will be beneficial. |
| data science apprenticeship program: Trade Apprenticeships , 1968 |
| data science apprenticeship program: Scientific Inquiry and Nature of Science Lawrence Flick, N.G. Lederman, 2007-11-03 This book synthesizes current literature and research on scientific inquiry and the nature of science in K-12 instruction. Its presentation of the distinctions and overlaps of inquiry and nature of science as instructional outcomes are unique in contemporary literature. Researchers and teachers will find the text interesting as it carefully explores the subtleties and challenges of designing curriculum and instruction for integrating inquiry and nature of science. |
| data science apprenticeship program: A New U Ryan Craig, 2018-09-11 Every year, the cost of a four-year degree goes up, and the value goes down. But for many students, there's a better answer. So many things are getting faster and cheaper. Movies stream into your living room, without ticket or concession-stand costs. The world's libraries are at your fingertips instantly, and for free. So why is a college education the only thing that seems immune to change? Colleges and universities operate much as they did 40 years ago, with one major exception: tuition expenses have risen dramatically. What's more, earning a degree takes longer than ever before, with the average time to graduate now over five years. As a result, graduates often struggle with enormous debt burdens. Even worse, they often find that degrees did not prepare them to obtain and succeed at good jobs in growing sectors of the economy. While many learners today would thrive with an efficient and affordable postsecondary education, the slow and pricey road to a bachelor's degree is starkly the opposite. In A New U: Faster + Cheaper Alternatives to College, Ryan Craig documents the early days of a revolution that will transform—or make obsolete—many colleges and universities. Alternative routes to great first jobs that do not involve a bachelor's degree are sprouting up all over the place. Bootcamps, income-share programs, apprenticeships, and staffing models are attractive alternatives to great jobs in numerous growing sectors of the economy: coding, healthcare, sales, digital marketing, finance and accounting, insurance, and data analytics. A New U is the first roadmap to these groundbreaking programs, which will lead to more student choice, better matches with employers, higher return on investment of cost and time, and stronger economic growth. |
| data science apprenticeship program: Occupational Projections and Training Data , 1976 |
| data science apprenticeship program: Data Science for Public Policy Jeffrey C. Chen, Edward A. Rubin, Gary J. Cornwall, 2021-09-01 This textbook presents the essential tools and core concepts of data science to public officials, policy analysts, and economists among others in order to further their application in the public sector. An expansion of the quantitative economics frameworks presented in policy and business schools, this book emphasizes the process of asking relevant questions to inform public policy. Its techniques and approaches emphasize data-driven practices, beginning with the basic programming paradigms that occupy the majority of an analyst’s time and advancing to the practical applications of statistical learning and machine learning. The text considers two divergent, competing perspectives to support its applications, incorporating techniques from both causal inference and prediction. Additionally, the book includes open-sourced data as well as live code, written in R and presented in notebook form, which readers can use and modify to practice working with data. |
| data science apprenticeship program: Higher Education's Road to Relevance Susan A. Ambrose, Laura A. Wankel, 2020-01-02 Explores the current context, role, and challenges of post-secondary education and presents options for promising pathways forward. The post-secondary educational system has undergone dramatic changes and experienced immense stress in the past two decades. Once regarded as the logical next step toward career opportunities and financial security, higher education is a subject of growing uncertainty for millions of people across the United States. It is more common than ever to question the return on investment, skyrocketing cost, and student debt burden of going to college. Prospective students, and many employers, increasingly view attending institutions of higher learning as inadequate preparation for entering the 21st century workforce. High-profile scandals—financial impropriety, sexual abuse, restrictions of free speech, among others—have further eroded public trust. In response to these and other challenges, leading voices are demanding strengthened accountability and measurable change. Higher Education's Road to Relevance illustrates why change is needed in post-secondary education and offers practical solutions to pressing concerns. The authors, internationally recognized experts in college-level teaching and learning innovation, draw heavily from contemporary research to provide an integrative approach for post-secondary faculty, staff, and administrators of all levels. This timely book helps readers identify the need for leadership in developing new networks and ecosystems of learning and workforce development. This valuable book will help readers: Understand the forces driving change in higher education Develop multiple pathways to create and credential self-directed learners Promote access to flexible, cost-effective, and relevant learning Adapt structures and pedagogies to address issues and overcome challenges Use an inclusive approach that extends to employers, K-12 educators, post-secondary educators, and policy-makers, among others Higher Education's Road to Relevance is a much-needed resource for college and university administrators, academic researchers, instructors and other faculty, and staff who support and interact with students. |
| data science apprenticeship program: The Success of Apprenticeships Jean-Luc Cerdin, Jean-Marie Peretti, 2020-02-05 Apprenticeships can offer apprentices, their teacher-tutors and business apprenticeship supervisors experiences that are rich in knowledge. The Success of Apprenticeships presents the observations and opinions of 48 actors regarding apprenticeships. These testimonies recount how apprenticeships allowed them to improve their expertise, their professional practices and their organization skills. This book also examines how their interactions in the work/study process allowed them not only to develop the skills of apprentices, but also the skills of those who accompanied them – the teacher-tutors and the business apprenticeships supervisors. The creation of an authentic community of apprentices subscribes to the formation of an ecosystem of learning, in which each individual harvests fruits in terms of the development of their personal abilities. |
| data science apprenticeship program: Workforce Education William B. Bonvillian, Sanjay E. Sarma, 2021-02-02 A roadmap for how we can rebuild America's working class by transforming workforce education and training. The American dream promised that if you worked hard, you could move up, with well-paying working-class jobs providing a gateway to an ever-growing middle class. Today, however, we have increasing inequality, not economic convergence. Technological advances are putting quality jobs out of reach for workers who lack the proper skills and training. In Workforce Education, William Bonvillian and Sanjay Sarma offer a roadmap for rebuilding America's working class. They argue that we need to train more workers more quickly, and they describe innovative methods of workforce education that are being developed across the country. |
| data science apprenticeship program: R for Data Science Hadley Wickham, Garrett Grolemund, 2016-12-12 Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true signals in your dataset Communicate—learn R Markdown for integrating prose, code, and results |
| data science apprenticeship program: Why Data Science Projects Fail Douglas Gray, Evan Shellshear, 2024-09-05 The field of artificial intelligence, data science, and analytics is crippling itself. Exaggerated promises of unrealistic technologies, simplifications of complex projects, and marketing hype are leading to an erosion of trust in one of our most critical approaches to making decisions: data driven. This book aims to fix this by countering the AI hype with a dose of realism. Written by two experts in the field, the authors firmly believe in the power of mathematics, computing, and analytics, but if false expectations are set and practitioners and leaders don’t fully understand everything that really goes into data science projects, then a stunning 80% (or more) of analytics projects will continue to fail, costing enterprises and society hundreds of billions of dollars, and leading to non-experts abandoning one of the most important data-driven decision-making capabilities altogether. For the first time, business leaders, practitioners, students, and interested laypeople will learn what really makes a data science project successful. By illustrating with many personal stories, the authors reveal the harsh realities of implementing AI and analytics. |
| data science apprenticeship program: Multivariable Calculus James Stewart, 2011-09-27 Success in your calculus course starts here! James Stewart's CALCULUS, 7e, International Metric texts are world-wide best-sellers for a reason: they are clear, accurate, and filled with relevant, real-world examples. With MULTIVARIABLE CALCULUS, 7e, International Metric Edition Stewart conveys not only the utility of calculus to help you develop technical competence, but also gives you an appreciation for the intrinsic beauty of the subject. His patient examples and built-in learning aids will help you build your mathematical confidence and achieve your goals in the course! |
| data science apprenticeship program: Department of Defense Appropriations for 1991: Automatic data processing programs United States. Congress. House. Committee on Appropriations. Subcommittee on Department of Defense, 1990 |
| data science apprenticeship program: United States Code United States, 2013 The United States Code is the official codification of the general and permanent laws of the United States of America. The Code was first published in 1926, and a new edition of the code has been published every six years since 1934. The 2012 edition of the Code incorporates laws enacted through the One Hundred Twelfth Congress, Second Session, the last of which was signed by the President on January 15, 2013. It does not include laws of the One Hundred Thirteenth Congress, First Session, enacted between January 2, 2013, the date it convened, and January 15, 2013. By statutory authority this edition may be cited U.S.C. 2012 ed. As adopted in 1926, the Code established prima facie the general and permanent laws of the United States. The underlying statutes reprinted in the Code remained in effect and controlled over the Code in case of any discrepancy. In 1947, Congress began enacting individual titles of the Code into positive law. When a title is enacted into positive law, the underlying statutes are repealed and the title then becomes legal evidence of the law. Currently, 26 of the 51 titles in the Code have been so enacted. These are identified in the table of titles near the beginning of each volume. The Law Revision Counsel of the House of Representatives continues to prepare legislation pursuant to 2 U.S.C. 285b to enact the remainder of the Code, on a title-by-title basis, into positive law. The 2012 edition of the Code was prepared and published under the supervision of Ralph V. Seep, Law Revision Counsel. Grateful acknowledgment is made of the contributions by all who helped in this work, particularly the staffs of the Office of the Law Revision Counsel and the Government Printing Office--Preface. |
| data science apprenticeship program: Move Parag Khanna, 2022-10-04 In the 60,000 years since people began colonizing the continents, a continuous feature of human civilization has been mobility. History is replete with seismic global events-pandemics and plagues, wars and genocides. Each time, after a great catastrophe, our innate impulse toward physical security compels us to move. The map of humanity isn't settled-not now, not ever. The filled-with-crises 21st century promises to contain the most dangerous and extensive experiment humanity has ever run on itself: As climates change, pandemics arrive, and economies rise and fall, which places will people leave and where will they resettle? Which countries will accept or reject them? How will the billions alive today, and the billions coming, paint the next map of human geography? Until now, the study of human geography and migration has been like a weather forecast. Move delivers an authoritative look at the climate of migration, the deep trends that will shape the grand economic and security scenarios of the future. For readers, it will be a chance to identify their location on humanity's next map-- |
| data science apprenticeship program: The Promise of Workplace Training for Non-college Bound Youth Damon Clark, René Fahr, 2001 |
| data science apprenticeship program: Transforming Trajectories for Women of Color in Tech National Academies of Sciences, Engineering, and Medicine, National Academies Of Sciences Engineeri, Policy and Global Affairs, Board on Higher Education and Workforce, Committee on Addressing the Underrepresentation of Women of Color in Tech, 2022-09-09 Demand for tech professionals is expected to increase substantially over the next decade, and increasing the number of women of color in tech will be critical to building and maintaining a competitive workforce. Despite years of efforts to increase the diversity of the tech workforce, women of color have remained underrepresented, and the numbers of some groups of women of color have even declined. Even in cases where some groups of women of color may have higher levels of representation, data show that they still face significant systemic challenges in advancing to positions of leadership. Research evidence suggests that structural and social barriers in tech education, the tech workforce, and in venture capital investment disproportionately and negatively affect women of color. Transforming Trajectories for Women of Color in Tech uses current research as well as information obtained through four public information-gathering workshops to provide recommendations to a broad set of stakeholders within the tech ecosystem for increasing recruitment, retention, and advancement of women of color. This report identifies gaps in existing research that obscure the nature of challenges faced by women of color in tech, addresses systemic issues that negatively affect outcomes for women of color in tech, and provides guidance for transforming existing systems and implementing evidence-based policies and practices to increase the success of women of color in tech. |
| data science apprenticeship program: Hydroelectric Plant Electrician National Learning Corporation, 2013 The Hydroelectric Plant Electrician Passbook(R) prepares you for your test by allowing you to take practice exams in the subjects you need to study. It provides hundreds of questions and answers in the areas that will likely be covered on your upcoming exam, including but not limited to: maintenance and repair of mechanical equipment; basic arithmetic; mechanical principles; safety procedures; and more. |
| data science apprenticeship program: Labour and Skills Demand in Alberta Insights Using Big Data Intelligence OECD, 2023-09-08 This report examines Alberta's labour market trends, focusing on the impact of economic downturns, the COVID-19 crisis, and digital transformation. This study uses real-time labour market data, drawn from online job postings, to offer a granular perspective on demand dynamics across various sectors and occupations. |
| data science apprenticeship program: Bulletin of the United States Bureau of Labor Statistics , 1976 |
| data science apprenticeship program: Bulletin of the United States Bureau of Labor Statistics United States. Bureau of Labor Statistics, 1980 |
| data science apprenticeship program: America’s Hidden Economic Engines Robert B. Schwartz, Rachel Lipson, 2023-06-06 Five in-depth case studies reveal the innovative practices that position U.S. community colleges as pathways to quality employment. In America’s Hidden Economic Engines, editors Robert B. Schwartz and Rachel Lipson spotlight community and technical colleges as institutions uniquely equipped to foster more equitable economic growth across America’s regions. As Schwartz and Lipson show, these colleges are the best-placed institutions to reverse the decades-long rise in US economic inequality by race, class, and geography. In the book, Harvard Project on Workforce researchers introduce detailed case studies of five institutions—Lorain County Community College in Ohio, Mississippi Gulf Coast Community College, Northern Virginia Community College, Pima Community College in Arizona, and San Jacinto Community College in Texas—that show what is possible when governments, employers, and communities invest in their community colleges’ economic and workforce development mission. These case studies reveal key institutional policies and practices, leadership behaviors, and organizational structures of successful collaborations between colleges and their regional partners in the public and private sector. Each case underscores how, although community colleges face distinct challenges based on local context, successful schools demonstrate a consistent focus on economic mobility and good jobs across all their programs and activities. In a concluding chapter, the editors champion community colleges as the most critical institutions for the future of US workforce development policy. |
| data science apprenticeship program: From 'Science in the Making' to Understanding the Nature of Science Mansoor Niaz, 2012-02-06 The Nature of Science is highly topical among science teacher educators and researchers. Increasingly, it is a mandated topic in state curriculum documents. This book draws together recent research on Nature of Science studies within a historical and philosophical framework suitable for students and teacher educators. Traditional science curricula and textbooks present science as a finished product. Taking a different approach, this book provides a glimpse of “science in the making” — scientific practice imbued with arguments, controversies, and competition among rival theories and explanations. Teaching about “science in the making” is a rich source of motivating students to engage creatively with the science curriculum. Readers are introduced to “science in the making” through discussion and analysis of a wide range of historical episodes from the early 19th century to early 21st century. Recent cutting-edge research is presented to provide insight into the dynamics of scientific progress. More than 90 studies from major science education journals, related to nature of science are reviewed. A theoretical framework, field tested with in-service science teachers, is developed for moving from ‘science in the making’ to understanding the Nature of Science. |
| data science apprenticeship program: Data Mining and Predictive Analytics Daniel T. Larose, 2015-02-19 Learn methods of data analysis and their application to real-world data sets This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. The authors apply a unified “white box” approach to data mining methods and models. This approach is designed to walk readers through the operations and nuances of the various methods, using small data sets, so readers can gain an insight into the inner workings of the method under review. Chapters provide readers with hands-on analysis problems, representing an opportunity for readers to apply their newly-acquired data mining expertise to solving real problems using large, real-world data sets. Data Mining and Predictive Analytics: Offers comprehensive coverage of association rules, clustering, neural networks, logistic regression, multivariate analysis, and R statistical programming language Features over 750 chapter exercises, allowing readers to assess their understanding of the new material Provides a detailed case study that brings together the lessons learned in the book Includes access to the companion website, www.dataminingconsultant, with exclusive password-protected instructor content Data Mining and Predictive Analytics will appeal to computer science and statistic students, as well as students in MBA programs, and chief executives. |
| data science apprenticeship program: The Complete Book of Colleges, 2020 Edition Princeton Review (COR), 2019-07-02 No one knows colleges better than The Princeton Review! Inside The Complete Book of Colleges, 2020 Edition, students will find meticulously researched information that will help them narrow their college search. |
| data science apprenticeship program: Some United States Activities Using Science and Technology for Development , 1979 |
| data science apprenticeship program: The Science of Effective Mentorship in STEMM National Academies of Sciences, Engineering, and Medicine, Policy and Global Affairs, Board on Higher Education and Workforce, Committee on Effective Mentoring in STEMM, 2020-01-24 Mentorship is a catalyst capable of unleashing one's potential for discovery, curiosity, and participation in STEMM and subsequently improving the training environment in which that STEMM potential is fostered. Mentoring relationships provide developmental spaces in which students' STEMM skills are honed and pathways into STEMM fields can be discovered. Because mentorship can be so influential in shaping the future STEMM workforce, its occurrence should not be left to chance or idiosyncratic implementation. There is a gap between what we know about effective mentoring and how it is practiced in higher education. The Science of Effective Mentorship in STEMM studies mentoring programs and practices at the undergraduate and graduate levels. It explores the importance of mentorship, the science of mentoring relationships, mentorship of underrepresented students in STEMM, mentorship structures and behaviors, and institutional cultures that support mentorship. This report and its complementary interactive guide present insights on effective programs and practices that can be adopted and adapted by institutions, departments, and individual faculty members. |
| data science apprenticeship program: Internet Links for Science Education Karen C. Cohen, 2012-12-06 Science teachers come in many varieties, but they share a common goal: to nurture learners. Over the past decade, we have learned a great deal about how to do this effectively. Of all this new (and some not so new) knowledge, what strikes me as most important is that learning occurs best within a context. Still, as obvious as that may seem, it is relatively rare in our high school science classrooms. The problem, of course, is that it is not easy to create a learning experience with hands-on relevance to the science under discussion. Science teachers, in addition to not having the the time, for the most part do not have the expertise or readily available resources. The solution lies in finding ways to bring scientists into the teaching/learning equation. Scientists teamed with teachers and their students represent a very real and rich opportunity to involve students in real science as practiced. Imagine a research book that gives examples of honest, science-research experiences for science-oriented students. What's more, imagine a book that includes examples where students are collaborating with scientists from all over the world on research projects, in person or via the Internet. Internet Linksfor Science Education does just that. It explores the role of the Internet and technol ogy in working student-scientist partnerships. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will enable a …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels to …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will enable a …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with minimum time …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, released in …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process from …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical barriers …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels to …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be collected, …
